M09 - Data Scientist

FPT Asia Pacific; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

FPT Asia Pacific is seeking a hands-on Data Scientist to support multiple product squads working with government agencies to redesign operational workflows and develop AI-enabled solutions. You will analyse large datasets, identify patterns and anomalies, develop machine learning models, build data pipelines, and deploy production-ready solutions in collaboration with engineers and domain experts.

The role requires strong Python/SQL, ML techniques, and the ability to translate business needs

Qualifications

• Bachelor's degree in data science, CS, statistics or related field.
• Proven experience in applied data science and predictive modelling.
• Strong Python and SQL skills for analysis and modeling.
• Experience deploying ML models to production and building data pipelines.

Responsibilities

• Analyse large operational datasets to identify patterns and anomalies.
• Develop and deploy ML models for risk assessment and workflow automation.
• Design data processing pipelines and feature engineering workflows.
• Experiment with anomaly detection, clustering, classification, and scoring.
• Collaborate with stakeholders to translate challenges into data-driven solutions.
• Monitor models for performance and data drift.
• Explore LLMs and AI techniques where appropriate.

Skills

Python

SQL

Machine Learning

Data Analysis

Feature Engineering

Model Deployment

Communication

Problem Solving

Team Collaboration

Education

Bachelor's degree in Data Science or related field

Tools

Git

API Integrations

Docker

CI/CD

ML Ops

Job description

We are seeking a Data Scientist to support multiple product squads working with government agencies to redesign operational workflows and develop AI-enabled solutions.

The successful candidate will be responsible for analysing large operational datasets, identifying patterns and anomalies, and developing machine learning models to support risk assessment, decision-making, and workflow automation.

This is a hands-on role that involves end-to-end data science activities, including exploratory data analysis, feature engineering, model development, data pipeline implementation, and production deployment.

Key Responsibilities

• Analyse large and complex operational datasets to identify patterns, anomalies, outliers, and potential risk indicators.
• Develop and implement machine learning techniques, including anomaly detection, clustering, segmentation, classification, and risk scoring.
• Build and optimise supervised machine learning models using historical data and labelled datasets.
• Perform data preprocessing, cleaning, transformation, and feature engineering using structured and unstructured data.
• Design and implement model evaluation frameworks, including precision, recall, false positives, false negatives, and other relevant performance metrics.
• Collaborate with business stakeholders and domain experts to translate operational challenges into data-driven solutions.
• Design, develop, and maintain scalable data processing pipelines and feature engineering workflows.
• Develop production-ready machine learning models and scoring pipelines, integrating them with applications developed by engineering teams.
• Monitor deployed models to ensure performance, data quality, reliability, and detection of data drift.
• Conduct exploratory data analysis to identify opportunities for process improvement, workflow redesign, and automation.
• Explore and apply Large Language Models (LLMs) and other AI techniques alongside traditional statistical and machine learning approaches where appropriate.
• Develop reusable analytical frameworks, models, and components to support multiple projects and government agency engagements.
• Work closely with engineers, product teams, and stakeholders to ensure successful deployment and adoption of data science solutions.

Requirements

• Bachelor's Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics, Information Systems, or a related discipline preferred.
• Proven experience in applied data science, statistical analysis, machine learning, and predictive modelling.
• Strong proficiency in Python and SQL for data analysis, manipulation, and model development.
• Hands-on experience with machine learning techniques, including anomaly detection, classification, clustering, and risk modelling.
• Experience working with large, complex, and unstructured operational or transactional datasets.
• Strong understanding of data preprocessing, feature engineering, statistical modelling, and model evaluation techniques.
• Experience developing data pipelines and deploying machine learning models into production environments.
• Ability to translate business and operational requirements into analytical problems and appropriate modelling approaches.
• Familiarity with software engineering practices, including Git, automated testing, and API integrations.
• Strong understanding of model performance metrics, trade-offs, and validation methodologies.
• Ability to communicate technical findings, model outcomes, and recommendations to non-technical stakeholders.
• Strong analytical, problem-solving, and critical-thinking skills.
• Ability to work independently and manage multiple projects in a fast-paced environment.
• Strong collaboration skills and a proactive approach to delivering practical, production-ready solutions.

Preferred Qualifications

• Experience with containerisation, CI/CD pipelines, DevOps, or MLOps practices.
• Familiarity with Large Language Models (LLMs), Generative AI, and AI-enabled applications.
• Experience integrating machine learning models into enterprise applications and operational workflows.
• Prior experience supporting government agencies or public sector digital transformation initiatives will be advantageous.